Method and apparatus for detecting malfunction of lidar, and method and apparatus for generating data therefor

Abstract

A method and an apparatus for detecting malfunction of LIDAR, and a method and an apparatus for generating data therefor are disclosed. According to an aspect of the present disclosure, there is provided a method for detecting malfunction of a LIDAR, including: acquiring point cloud data from the LIDAR; determining whether the number of points included in the point cloud data is less than a point count threshold; and determining whether the LIDAR has a fault based on the point cloud data by using a LIDAR malfunction detection model when the number of points is greater than or equal to the point count threshold, wherein the LIDAR malfunction detection model is a pre-trained model based on a normal dataset and at least one LIDAR-abnormal dataset, the normal dataset includes preset statistics and point cloud data acquired from the LIDAR in a normal state, and the LIDAR-abnormal dataset includes point cloud data generated based on the normal dataset.

What is claimed is: 
     
         1 . A method for detecting malfunction of a LIDAR, comprising:
 acquiring point cloud data from the LIDAR;   determining whether the number of points comprised in the point cloud data is less than a point count threshold; and   determining whether the LIDAR has a fault based on the point cloud data by using a LIDAR malfunction detection model when the number of points is greater than or equal to the point count threshold,   wherein the LIDAR malfunction detection model is a pre-trained model based on a normal dataset and at least one LIDAR-abnormal dataset,   the normal dataset comprises preset statistics and point cloud data acquired from the LIDAR in a normal state, and   
       the LIDAR-abnormal dataset comprises point cloud data generated based on the normal dataset. 
     
     
         2 . The method of  claim 1 , further comprising:
 determining whether a confidence of the LIDAR malfunction detection model is less than a confidence threshold;   not increasing a LIDAR error accumulation count when the confidence of the LIDAR malfunction detection model is less than the confidence threshold, and increasing the LIDAR error accumulation count by 1 when the confidence of the LIDAR malfunction detection model is greater than or equal to the confidence threshold;   determining whether the LIDAR error accumulation count is greater than a LIDAR error accumulation threshold; and   determining that the LIDAR has a fault when the LIDAR error accumulation count is greater than the LIDAR error accumulation threshold.   
     
     
         3 . The method of  claim 1 , further comprising:
 preprocessing the point cloud data,   wherein the preprocessing comprises:   calculating a distance from an origin of a LIDAR coordinate system to each point comprised in the point cloud data;   sorting the points in ascending order of distance based on the calculation result; and   extracting and sampling a predetermined number of points based on the sorted result.   
     
     
         4 . A method for detecting malfunction of a LIDAR, comprising:
 acquiring point cloud data from the LIDAR;   determining whether the number of points comprised in the point cloud data is less than a point count threshold;   determining whether a duration taken to receive the point cloud data is less than a point cloud data reception time threshold when the number of points is greater than or equal to the point count threshold;   not increasing a timer error accumulation count when the duration taken to receive the point cloud data is less than the point cloud data reception time threshold, and increasing the timer error accumulation count by 1 when the duration taken to receive the point cloud data is greater than or equal to the point cloud data reception time threshold;   determining whether the timer error accumulation count is greater than a timer error accumulation count threshold; and   determining that there is a fault in acquiring the point cloud data from the LIDAR, rather than a fault in the LIDAR when the timer error accumulation count is greater than the timer error accumulation count threshold.   
     
     
         5 . An apparatus for detecting malfunction of a LIDAR, comprising:
 at least one memory storing instructions; and at least one processor,   wherein the at least one processor is configured to execute the instructions to perform:   acquiring point cloud data from the LIDAR;   determining whether a number of points comprised in the point cloud data is less than a point count threshold; and   determining whether the LIDAR has a fault based on the point cloud data by using a LIDAR malfunction detection model when the number of points is greater than or equal to the point count threshold,   wherein:   the LIDAR malfunction detection model is a pre-trained model based on a normal dataset and at least one or more LIDAR-abnormal dataset,   the normal dataset comprises preset statistics and point cloud data acquired from the LIDAR in a normal state, and   the LIDAR-abnormal dataset comprises point cloud data generated based on the normal dataset.